1. Field of the Invention
The present invention relates to a material recognizing apparatus provided with a data classifying apparatus that classifies input data into categories.
2. Description of the Related Art
Conventionally there is a data classifying apparatus that classifies input data into categories that an application requires. The conventional data classifying apparatus first obtains a vector of the input data and a representative vector of each category in classifying the input data into a corresponding category. The representative vector of a category is a barycentric vector of vectors of all model data contained in the category.
Next, the conventional data classifying apparatus compares the vector of the input data with the representative vector of the category, and detects a category having the representative vector having the highest degree of similarity with the vector of the input data. Then, the conventional data classifying apparatus classifies the input data into the category with the representative vector having the highest degree of similarity.
However, there is a case where the representative vector of a category does not indicate an accurate vector distribution. For example, as illustrated in FIG. 19, a category A has a wide distribution with an empty space in the center portion, while a category B has a small distribution disposed around the center portion of the category A. In this case, a position of the representative vector of the category A is point a, the representative vector of the category B is point b, and thereby the respective positions of the representative vectors become very close. When input data represented by a vector of point c is input, the input data is determined to have a high degree of similarity with the representative vector of the category A, and thereby is classified into the category A. However, taking account of respective distributions of categories A and B, the input data actually has a high degree of similarity with the category B, and should be classified into the category B.
Thus, in the conventional data classifying apparatus, there occurs a case that input data is not classified into a proper category.